lme4 (version 1.1-7)

nlmer: Fit Nonlinear Mixed-Effects Models

Description

Fit a nonlinear mixed-effects model

Usage

nlmer(formula, data = NULL, control = nlmerControl(),
    start = NULL, verbose = 0L, nAGQ = 1L, subset, weights,
    na.action, offset, contrasts = NULL,
    devFunOnly = FALSE, ...)

Arguments

formula

a three-part nonlinear mixed model formula, of the form resp ~ Nonlin(...) ~ fixed + random, where the third part is similar to the RHS formula of, e.g., lmer.

start

starting estimates for the nonlinear model parameters, as a named numeric vector or as a list with components [object Object],[object Object]

...

other potential arguments. A method argument was used in earlier versions of the package. Its functionality has been replaced by the nAGQ argument.

data

an optional data frame containing the variables named in formula. By default the variables are taken from the environment from which lmer is called. While data is optional, the package authors strongly

control

a list (of correct class, resulting from lmerControl() or glmerControl() respectively) containing control parameters, including the nonlinear

verbose

integer scalar. If > 0 verbose output is generated during the optimization of the parameter estimates. If > 1 verbose output is generated during the individual PIRLS steps.

nAGQ

integer scalar - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Defaults to 1, corresponding to the Laplace approximation. Values greater than 1 produce greater accuracy in the evalua

subset

an optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the r

weights

an optional vector of prior weights to be used in the fitting process. Should be NULL or a numeric vector.

na.action

a function that indicates what should happen when the data contain NAs. The default action (na.omit, inherited from the 'factory fresh' value of getOption("na.action")) strips any observations with any missi

offset

this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

devFunOnly

logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return exactly the same values on subsequent calls (but the results should always

Details

Fit nonlinear mixed-effects models, such as those used in population pharmacokinetics.

Examples

Run this code

## nonlinear mixed models --- 3-part formulas ---
## 1. basic nonlinear fit. Use stats::SSlogis for its
## implementation of the 3-parameter logistic curve.
## "SS" stands for "self-starting logistic", but the
## "self-starting" part is not currently used by nlmer ... 'start' is
## necessary
startvec <- c(Asym = 200, xmid = 725, scal = 350)
(nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
             Orange, start = startvec))
## 2. re-run with "quick and dirty" PIRLS step
(nm1a <- update(nm1, nAGQ = 0L))
## 3. Fit the same model with a user-built function:
## a. Define formula
nform <- ~Asym/(1+exp((xmid-input)/scal))
## b. Use deriv() to construct function:
nfun <- deriv(nform,namevec=c("Asym","xmid","scal"),
              function.arg=c("input","Asym","xmid","scal"))
nm1b <- update(nm1,circumference ~ nfun(age, Asym, xmid, scal)  ~ Asym | Tree)
## 4. User-built function without using derivs():
##    derivatives could be computed more efficiently
##    by pre-computing components, but these are essentially
##    the gradients as one would derive them by hand
nfun2 <- function(input, Asym, xmid, scal) {
    value <- Asym/(1+exp((xmid-input)/scal))
    grad <- cbind(Asym=1/(1+exp((xmid-input)/scal)),
              xmid=-Asym/(1+exp((xmid-input)/scal))^2*1/scal*
                    exp((xmid-input)/scal),
              scal=-Asym/(1+exp((xmid-input)/scal))^2*
                     -(xmid-input)/scal^2*exp((xmid-input)/scal))
    attr(value,"gradient") <- grad
    value
}
stopifnot(all.equal(attr(nfun(2,1,3,4),"gradient"),
                    attr(nfun(2,1,3,4),"gradient")))
nm1c <- update(nm1,circumference ~ nfun2(age, Asym, xmid, scal)  ~ Asym | Tree)

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